A Technology for Analyzing the Transformation Process of University Students’ Learning Outcomes Based on Bloom’s Taxonomy

Tatyana V. Zykova; Yuliya V. Vainshtein; Mikhail V. Noskov

doi:10.2478/cait-2026-0002

Abstract

This paper presents a technology for analyzing the transformation of student learning outcomes (competencies) at different stages of mastering an educational program. The goal is a comprehensive approach for analyzing and predicting achievements by integrating data from curricula and the Learning Management System (LMS) Moodle digital footprints. Methods include graph analysis to model competency structures, machine learning (probabilistic matrix factorization for data imputation), Bloom’s taxonomy-based classification, and multidimensional data visualization. A general research framework is presented, describing the process of processing data from various sources, including the LMS Moodle and the “Undergraduate Plans” information system, followed by data visualization, including integrated data, in the BI system Yandex DataLens. The result is a novel approach for analyzing competency development and a software module for creating an automated decision support system in the educational process, based on the analysis of learning data.

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A Technology for Analyzing the Transformation Process of University Students’ Learning Outcomes Based on Bloom’s Taxonomy

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